Publications
2019
1.
Biswas, Rubel; González-Castro, Victor; Fidalgo, Eduardo; Chaves, Deisy
Boosting child abuse victim identification in Forensic Tools with hashing techniques Artículo de revista
En: V Jornadas Nacionales de Investigación en Ciberseguridad, vol. 1, pp. 344–345, 2019.
Resumen | Enlaces | BibTeX | Etiquetas: Child Sexual Abuse, CSA, Face Identification, Forensic Tools, Occluded Face Recognition, perceptual hashing
@article{biswas_boosting_2019,
title = {Boosting child abuse victim identification in Forensic Tools with hashing techniques},
author = {Rubel Biswas and Victor González-Castro and Eduardo Fidalgo and Deisy Chaves},
url = {https://d1wqtxts1xzle7.cloudfront.net/82369096/JNIC2019_paper_11-libre.pdf?1647729671=&response-content-disposition=inline%3B+filename%3DBoosting_child_abuse_victim_identificati.pdf&Expires=1739810458&Signature=AaFk3CnTgqPwNnr4OrQ5dsZkqjASKk7CkhuGAp8EOF-QuWpTRVAHTZT4xLl1lS1NbnaDHI9p6jPc9VBhUkfmf0tLOACFOMUePTJRQbAXBLbmVv6LRjudXB1eBxP-MFeBs5vE0Ok4QaUFalGTOmM6hBXQqZC1Wb5tMxsKZPEB8eI6SxboBVqqlinMT9wyAbjykITOSmqsRSTHjOH9vHL~SZ5-PuVIbmT82w7vTdnCvovPoSwA6rmGO~IICQINI0mvYiwRYtdYVDSodTg4W7WVfst3jrcVgK76BKJHYUnH6A1HqYO38mm0MEIeXmbTSpMERXAEkc2Ja-tnkB2pyUR-Mg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2019},
date = {2019-01-01},
journal = {V Jornadas Nacionales de Investigación en Ciberseguridad},
volume = {1},
pages = {344–345},
abstract = {This work presents a new scheme for identifying occluded faces using perceptual image hashing, which differs from traditional methods by eliminating the need for prior training of a facial model. The proposed method combines frequency coefficients and statistical image information to enhance the recognition accuracy of occluded faces. The approach aims to improve face recognition in forensic applications, such as identifying victims in Child Sexual Abuse (CSA) materials. Experimental results demonstrate that the new method outperforms previous methods in occluded face identification using the LFW database.},
keywords = {Child Sexual Abuse, CSA, Face Identification, Forensic Tools, Occluded Face Recognition, perceptual hashing},
pubstate = {published},
tppubtype = {article}
}
This work presents a new scheme for identifying occluded faces using perceptual image hashing, which differs from traditional methods by eliminating the need for prior training of a facial model. The proposed method combines frequency coefficients and statistical image information to enhance the recognition accuracy of occluded faces. The approach aims to improve face recognition in forensic applications, such as identifying victims in Child Sexual Abuse (CSA) materials. Experimental results demonstrate that the new method outperforms previous methods in occluded face identification using the LFW database.
2017
2.
Gangwar, Abhishek; Fidalgo, Eduardo; Alegre, Enrique; González-Castro, Víctor
Pornography and child sexual abuse detection in image and video: A comparative evaluation Artículo de revista
En: 2017, (Publisher: IET Digital Library).
Resumen | Enlaces | BibTeX | Etiquetas: CSA, deep learning, Image classification, pornography detection
@article{gangwar_pornography_2017,
title = {Pornography and child sexual abuse detection in image and video: A comparative evaluation},
author = {Abhishek Gangwar and Eduardo Fidalgo and Enrique Alegre and Víctor González-Castro},
url = {https://digital-library.theiet.org/doi/10.1049/ic.2017.0046},
year = {2017},
date = {2017-01-01},
abstract = {This paper reviews automatic detection methods for pornography and Child Sex Abuse (CSA) material, particularly in sensitive environments like educational or work settings. It evaluates five pornography detection approaches, including traditional skin detection and modern deep learning techniques, using two publicly available pornographic databases. The study finds that methods utilizing multiple features perform better than those relying on single features and that deep learning-based methods outperform traditional approaches, achieving state-of-the-art results. Additionally, the methods were tested on real-world CSA material provided by the Spanish Police.},
note = {Publisher: IET Digital Library},
keywords = {CSA, deep learning, Image classification, pornography detection},
pubstate = {published},
tppubtype = {article}
}
This paper reviews automatic detection methods for pornography and Child Sex Abuse (CSA) material, particularly in sensitive environments like educational or work settings. It evaluates five pornography detection approaches, including traditional skin detection and modern deep learning techniques, using two publicly available pornographic databases. The study finds that methods utilizing multiple features perform better than those relying on single features and that deep learning-based methods outperform traditional approaches, achieving state-of-the-art results. Additionally, the methods were tested on real-world CSA material provided by the Spanish Police.